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No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data.

, , , , , and . NeurIPS, page 5972-5984. (2021)

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MetaFormer is Actually What You Need for Vision., , , , , , , and . CoRR, (2021)Smarter Warehouse., , , , , , and . ICDE Workshops, page 1-8. IEEE, (2022)Clockwork: A Delay-Based Global Scheduling Framework for More Consistent Landing Times in the Data Warehouse., , , , , , , and . KDD, page 3627-3637. ACM, (2021)MetaFormer Baselines for Vision., , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 46 (2): 896-912 (February 2024)Comparative analysis of machine learning algorithms and statistical models for predicting crown width of Larix olgensis., , , , and . Earth Sci. Informatics, 15 (4): 2415-2429 (December 2022)MetaFormer is Actually What You Need for Vision., , , , , , , and . CVPR, page 10809-10819. IEEE, (2022)No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data., , , , , and . NeurIPS, page 5972-5984. (2021)MetaFormer Baselines for Vision., , , , , , , and . CoRR, (2022)Fuelling the future: microbial engineering for the production of sustainable biofuels, , , and . Nature Reviews Microbiology, 14 (5): 288--304 (Mar 30, 2016)MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection., , , , , , and . WWW, page 2507-2513. ACM / IW3C2, (2020)